# How AI Models Learn from Your Health Data Without Sharing It > This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data. - **Patent:** US 11853891 - **Original title:** System and method with federated learning model for medical research applications - **Owner:** Sharecare AI - **Granted:** 2023 - **Status:** Active - **Times cited:** 1 - **Field:** consumer_electronics, software, ai_ml, telecommunications, healthcare ## What it does This system uses a method called federated learning to train artificial intelligence models. It involves multiple 'edge devices' (like your smartphone) that each have a program called a 'federated learner' running a 'deep neural network' (DNN). This DNN predicts characteristics about you, such as your mood or age, from your recorded data like pictures or audio, as mentioned in claims 1, 2, 5, and 7. If the prediction is wrong, you can correct it. Your device then uses your private data and corrections to update its part of the DNN model. Instead of sending your raw data, only a 'modified part of the modified tensor' (a small, updated piece of the AI model) is sent to a central 'FL aggregator'. The aggregator combines these updates from many devices to create a better, shared model, which it then sends back to all the devices. For example, claim 1 specifies that if the data is a picture, the system only performs this update training if a face is detected in the image. ## What it does NOT cover - It does not cover systems where raw user data, like pictures or audio recordings, is directly uploaded to a central server for training. - It does not cover AI training systems that do not perform 'update training' of the deep neural network directly on the end user's device. - It does not cover federated learning systems that send the entire model parameters from the edge device to the aggregator, rather than just a 'modified part'. - It does not cover systems that use picture data for training without first detecting a face in the picture, as specified in claim 1. - It does not cover federated learning without an 'FL aggregator' that combines updates from multiple edge devices to produce a new base model. ## The clever bit The clever bit is enabling AI models to learn from highly sensitive personal data, like facial images or voice recordings, directly on a user's device. This on-device training, combined with the selective sharing of only 'modified parts' of the model, allows for continuous improvement of a shared AI without ever exposing individual raw data to a central server. The specific requirement in claim 1 to only train on pictures if a face is detected adds a layer of focused privacy and relevance. ## Real-world examples 1. Google's Gboard keyboard for next-word prediction 2. Apple's Siri voice recognition improvements 3. NVIDIA Clara Federated Learning platform for healthcare 4. Various research initiatives in personalized health and diagnostics 5. Sharecare AI applications for health management ## Why it matters This patent addresses a significant challenge in medical research: how to use vast amounts of sensitive personal health data to train powerful AI models without compromising individual privacy. By keeping raw data on user devices and only sharing model updates, it enables the development of more accurate diagnostic and predictive tools. This approach is crucial for advancing personalized medicine and public health initiatives while adhering to strict data privacy regulations. ## Frequently asked questions ### What does How AI Models Learn from Your Health Data Without Sharing It cover? This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data. ### Who owns patent US 11853891? Sharecare AI owns this patent, granted in 2023. ### When does this patent expire? This patent is expected to expire on March 11, 2040, when the invention enters the public domain. ### What is patent US 11853891 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? This patent addresses a significant challenge in medical research: how to use vast amounts of sensitive personal health data to train powerful AI models without compromising individual privacy. By keeping raw data on user devices and only sharing model updates, it enables the development of more accurate diagnostic and predictive tools. This approach is crucial for advancing personalized medicine and public health initiatives while adhering to strict data privacy regulations. ### What does this patent NOT cover? It does not cover systems where raw user data, like pictures or audio recordings, is directly uploaded to a central server for training. **Full plain-English explainer:** https://patentbrief.org/patent/us/11853891/system-and-method-with-federated-learning-model-for-medical-research-application **Original patent:** https://patents.google.com/patent/US11853891 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How Devices Train Shared AI Models While Keeping Your Data Private](https://patentbrief.org/patent/us/12443890/partially-local-federated-learning) — This patent describes a method for training a machine learning model across many devices, where each device keeps some parts of the model and its data private, only sharing updates for the common, global parts of the model. - [How Devices Securely Share Machine Learning Model Updates](https://patentbrief.org/patent/us/12626144/data-processing-method-federated-learning-training-method-and-related-apparatus-) — This patent describes a method for multiple devices to train a machine learning model using their own data and then securely share parts of the trained model with each other before sending it to a central server, improving data privacy. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [How Training Systems Share AI Knowledge Without Sharing Private Student Data](https://patentbrief.org/patent/us/11915111/federated-machine-learning-in-adaptive-training-systems) — This patent describes a system where different student training centers can improve their AI models by sharing general learning patterns, not private student data, to make training better for everyone. - [Training AI Models Together with Unlabeled Data Using a Teacher](https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning) — This patent describes a way for multiple AI systems to learn together from data that hasn't been manually labeled, using a 'teacher' AI to create temporary labels for a 'student' AI.